Comparison between different Regional Climate Models applied to the present climate (1995-2005) of Greenland

In the context of climate change, the Greenland Ice Sheet (GrIS) plays an important role in sea level variation and oceanic thermohaline circulation changes. Unfortunately, Global Climate Models do not illustrate enough the characteristics of Greenland. To solve that, specific RCMs have been developed to take into account the features of polar regions.In this project, we compare three RCMs : the MAR model, the RACMO model and the Weather Research and Forecasting (WRF) model. WRF is an open source model developed by the Mesoscale and Microscale Meteorology Division of NCAR. WRF has been modified for polar regions by the Ohio State University. The key modifications are changes of surface energy balance and heat transfer, and an implementation of sea ice thickness, snow thickness and seasonally-varying sea ice albedo in the surface module (Noah LSM). We use here the standard WRF (version 3.2.1) and its polar optimization (called polar WRF). The MAR version tuned for the GrIS and coupled with a 1D surface scheme called SISVAT (for Soil Ice Snow Vegetation Atmosphere Transfer) is compared here. The version of RACMO is a specific version for the Greenland climate, RACMO2/GR. This model contains a special surface module for snow-ice treatment and other modifications concerning, for example, the surface turbulence heat flux or the surface roughness.The comparison is made on a domain centered on Greenland at a 25-km horizontal resolution over the 1995-2005 period when AutomaticWeather Station (AWS) measurements are available from the Greenland Climate NETwork (GC-NET). Statistics (mean, bias, RMSE, correlation coefficient) are calculated for the near-surface temperature, surface pressure, 10m-wind speed and specific humidity for winter (October to April) and summer (May to September). In addition, the modeled snowfall are evaluated with ice core-based snow accumulation climatologies.Comparison shows a significant improvement from RCMs compared to the reanalyses (NCEP2 and ERAINTERIM) in respect to the AWS measurements. RACMO and MAR seem to compare better with observations than WRF. However, we note a significant improvement between WRF and polarWRF

Decomposing Executive Function into Distinct Processes Underlying Human Decision Making.

peer reviewedExecutive function (EF) consists of higher level cognitive processes including working memory, cognitive flexibility, and inhibition which together enable goal-directed behaviors. Many neurological disorders are associated with EF dysfunctions which can lead to suboptimal behavior. To assess the roles of these processes, we introduce a novel behavioral task and modeling approach. The gamble-like task, with sub-tasks targeting different EF capabilities, allows for quantitative assessment of the main components of EF. We demonstrate that human participants exhibit dissociable variability in the component processes of EF. These results will allow us to map behavioral outcomes to EEG recordings in future work in order to map brain networks associated with EF deficits. Clinical relevance- This work will allow us to quantify EF deficits and corresponding brain activity in patient populations in future work

Internal states as a source of subject-dependent movement variability and their representation by large-scale networks

AbstractA human’s ability to adapt and learn relies on reflecting on past performance. Such reflections form latent factors called internal states that induce variability of movement and behavior to improve performance. Internal states are critical for survival, yet their temporal dynamics and neural substrates are less understood. Here, we link internal states with motor performance and neural activity using state-space models and local field potentials captured from depth electrodes in over 100 brain regions. Ten human subjects performed a goal-directed center-out reaching task with perturbations applied to random trials, causing subjects to fail goals and reflect on their performance. Using computational methods, we identified two internal states, indicating that subjects kept track of past errors and perturbations, that predicted variability in reaction times and speed errors. These states granted access to latent information indicative of how subjects strategize learning from trial history, impacting their overall performance. We further found that large-scale brain networks differentially encoded these internal states. The dorsal attention network encoded past errors in frequencies above 100 Hz, suggesting a role in modulating attention based on tracking recent performance in working memory. The default network encoded past perturbations in frequencies below 15 Hz, suggesting a role in achieving robust performance in an uncertain environment. Moreover, these networks more strongly encoded internal states and were more functionally connected in higher performing subjects, whose learning strategy was to respond by countering with behavior that opposed accumulating error. Taken together, our findings suggest large-scale brain networks as a neural basis of strategy. These networks regulate movement variability, through internal states, to improve motor performance.Key pointsMovement variability is a purposeful process conjured up by the brain to enable adaptation and learning, both of which are necessary for survival.The culmination of recent experiences—collectively referred to as internal states—have been implicated in variability during motor and behavioral tasks.To investigate the utility and neural basis of internal states during motor control, we estimated two latent internal states using state-space representation that modeled motor behavior during a goal-directed center-out reaching task in humans with simultaneous whole-brain recordings from intracranial depth electrodes.We show that including these states—based on error and environment uncertainty—improves the predictability of subject-specific variable motor behavior and reveals latent information related to task performance and learning strategies where top performers counter error scaled by trial history while bottom performers maintain error tendencies.We further show that these states are encoded by the large-scale brain networks known as the dorsal attention network and default network in frequencies above 100 Hz and below 15 Hz but found neural differences between subjects where network activity closely modulates with states and exhibits stronger functional connectivity for top performers.Our findings suggest the involvement in large-scale brain networks as a neural basis of motor strategy that orchestrates movement variability to improve motor performance.</jats:list-item

Medical treatment of renal cancer: new horizons.

Renal cell carcinoma (RCC) makes up 2-3% of adult cancers. The introduction of tyrosine kinase inhibitors (TKIs) and mammalian target of rapamycin inhibitors in the mid-2000s radically changed the management of RCC. These targeted treatments superseded immunotherapy with interleukin-2 and interferon. The pendulum now appears to be shifting back towards immunotherapy, with the evidence of prolonged overall survival of patients with metastatic RCC on treatment with the anti-programmed cell death 1 ligand monoclonal antibody, nivolumab. Clinical prognostic criteria aid prediction of relapse risk for resected localised disease. Unfortunately, for patients at high risk of relapse, no adjuvant treatment has yet shown benefit, although further trials are yet to report. Clinical prognostic models also have a role in the management of advanced disease; now there is a pressing need for predictive biomarkers to direct therapy. Treatment selection for metastatic disease is currently based on histology, prognostic group and patient preference based on side effect profile. In this article, we review the current medical and surgical management of localised, oligometastatic and advanced RCC, including side effect management and the evidence base for management of poor-risk and non-clear cell disease. We discuss recent results from clinical trials and how these are likely to shape future practice and a renaissance of immunotherapy for renal cell cancer

Characterization of Geographical and Meteorological Parameters

[EN]This chapter is devoted to the introduction of some geographical and meteorological information involved in the numerical modeling of wind fields and solar radiation. First, a brief description of the topographical data given by a Digital Elevation Model and Land Cover databases is provided. In particular, the Information System of Land Cover of Spain (SIOSE) is considered. The study is focused on the roughness length and the displacement height parameters that appear in the logarithmic wind profile, as well as in the albedo related to solar radiation computation. An extended literature review and characterization of both parameters are reported. Next, the concept of atmospheric stability is introduced from the Monin–Obukhov similarity theory to the recent revision of Zilitinkevich of the Neutral and Stable Boundary Layers (SBL). The latter considers the effect of the free-flow static stability and baroclinicity on the turbulent transport of momentum and of the Convective Boundary Layers (CBL), more precisely, the scalars in the boundary layer, as well as the model of turbulent entrainment